What is easier or more efficient to summarize voice or text? [DP/RN]
Summarizing text is always going to be 'easier or more efficient' than voice simply because voice requires the additional step of converting to text.  That doesn't tell you anything about accuracy.

From an article published on June 1, 2017, Google’s speech recognition is now almost as accurate as humans:
"According to Mary Meeker’s annual Internet Trends Report, Google’s machine learning-backed voice recognition — as of May 2017 — has achieved a 95% word accuracy rate for the English language. That current rate also happens to be the threshold for human accuracy."

If you need this kind of accuracy check out Google's Cloud Speech API.  There is even a speech to text feature on the web page.

Given a speech-to-text conversion accuracy of 95%, voice will be 5% less accurate than text if everything else is equal but it usually isn't.  People generally write better text, such as in documents or emails, than when they speak unless of course they are giving a formal lecture, or talking in a formal meeting.  If one is analyzing text messages, Tweets, or threads found in typical informal forums, you will find very poor quality in grammar, spelling, vocabulary, and punctuation.  The answer to your question will depend on the source of your text. 

In another article, dated November 13, 2017, Why 100% Accuracy Is Not Available With Speech Recognition Software Alone, the author gives some reasons, albeit for transcription software which has a special purpose, why there will always be some errors due to:


Speech Patterns and Accents - Regional variations exist, for example English speakers in Boston sound different than Kentucky.  How does the software handle slurred speech or when a person blends their words?
Grammar and Punctuation - speech recognition software doesn't know where a period, comma, or semi-colon belongs
Homonyms and unusual words - "Speech processing software can only recognize words and phrases that it has specifically been trained to recognize."
Ambient Noise, Overlapping Speech, and Number of Speakers


To address your last question about where the technology is going...
Four days ago a paper by Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria entitled Recent Trends in Deep Learning Based
Natural Language Processing was published which gives some of the answers.  

From the 'Conclusion' section:
With distributed representation, various deep models have
become the new state-of-the-art methods for NLP problems.
Supervised learning is the most popular practice in recent
deep learning research for NLP. In many real-world scenarios,
however, we have unlabeled data which require advanced
unsupervised or semi-supervised approaches. In cases where
there is lack of labeled data for some particular classes or the
appearance of a new class while testing the model, strategies
like zero-shot learning should be employed. These learning
schemes are still in their developing phase but we expect deep
learning based NLP research to be driven in the direction of
making better use of unlabeled data. We expect such trend to
continue with more and better model designs. We expect to
see more NLP applications that employ reinforcement learning
methods, e.g., dialogue systems. We also expect to see more
research on multimodal learning [167] as, in the real world,
language is often grounded on (or correlated with) other
signals.

Finally, we expect to see more deep learning models whose
internal memory (bottom-up knowledge learned from the data)
is enriched with an external memory (top-down knowledge
inherited from a KB). Coupling symbolic and sub-symbolic AI
will be key for stepping forward in the path from NLP to natural
language understanding. Relying on machine learning, in
fact, is good to make a ‘good guess’ based on past experience,
because sub-symbolic methods encode correlation and their
decision-making process is probabilistic.